Research software

Choosing the right software for use in a research software project can be challenging. How do we know which software is both fit for purpose and provides a sound basis for our project for the foreseeable future? And, how do we make such a choice given that the time and effort to explore what could be myriad alternatives may be limited?

This was a challenge we faced in the RiboViz project, a multi-disciplinary team of biologists, bioinformaticians and research software engineers based at EPCC and The Wallace Lab at University of Edinburgh, The Shah Lab at Rutgers University, and The Lareau Lab at University of California, Berkeley. RiboViz is an open source package to help us develop our understanding of protein synthesis via analysis of ribosome profiling data. At the heart of RiboViz is an analysis workflow, implemented in a Python script. To conform to best practices for scientific computing, which recommend the use of build tools to automate workflows and to reuse code instead of rewriting it, we sought to reimplement this workflow within a bioinformatics workflow management system.

Developing for the real world

As part of a recent ARCHER eCSE project I developed a new parallelisation strategy for a computational simulation application to enable it to scale efficiently to larger process counts. We managed to significantly reduce the parallel overheads, so the code was accepted into the main repository for users to exploit.

The Software Sustainability Institute (SSI) is seeking applications to its Open Call for Projects, a free service that allows researchers to propose joint software consultancy projects with the Institute.

If you write code as part of your research and you are successful in your application you can get the SSI to help you improve your development processes and/or your code's sustainability at zero cost to yourself. Note that the call closes on the 5th December 2014.

Do you use scientific codes in your research? Are the things you can do with it limited by the execution time? The code has been parallelised but does not scale well? How should you go about improving the performance? What can you do when you do not have full understanding of the code? There are some general steps that can be taken to improve the performance of parallelised codes. In this article I will describe briefly the process I have undertaken to optimise the parallel performance of a computational chemistry package, TINKER, as part of the EPCC/SSI APES project.

I'm happy to announce that the Software Sustainability Institute's campaign to gain recognition for research software engineers is featured in this week's Times Higher Education. The Software Sustainability Institute is led from its base at EPCC and works in collaboration with the universities of Manchester, Oxford and Southampton.